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Electronics
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Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition
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Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3515

Special Issue Editors

Prof. Dr. Yanping Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Gonzaga University, Spokane, WA 99258, USA
Interests: computer networks; bio-computing; computer security; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Jianjun Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of North Georgia, Oakwood, GA 30566, USA
Interests: computer networks; management information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wenlin Han

E-Mail Website
Guest Editor
Department of Computer Science, California State University, Fullerton, CA 92831, USA
Interests: cybersecurity; blockchain; metaverse; Web3
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) have revolutionized all industries and greatly changed people’s daily lives. Intelligent systems supported by AI can learn from massive data, structured or unstructured, open domain or domain-specific, at a scale we have never seen before. A broad spectrum of disciplines have embraced AI, producing numerous exciting applications across various technological fields, such as smart cities, transportation, health care, finance, criminal justice, and many others.

This Special Issue aims to collect high-quality research papers presenting theoretical and applied research findings for intelligent systems and their cross-disciplinary applications. The topics include, but are not limited to:

  • AI in cybersecurity;
  • Emerging applications in natural language processing;
  • AI in the internet of things;
  • AI in finance;
  • AI in smart transportation and smart cities;
  • AI in health care;
  • AI in bioinformatics;
  • AI in education.

Prof. Dr. Yanping Zhang
Dr. Jianjun Yang
Dr. Wenlin Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • intelligent systems
  • natural language processing
  • cybersecurity
  • smart cities
  • bioinformatics
  • health care
  • education

Related Special Issue

Published Papers (5 papers)

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Research

18 pages, 2961 KiB  
Article
Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning
by Bálint Kővári, Bálint Pelenczei, István Gellért Knáb and Tamás Bécsi
Electronics 2024, 13(11), 2058; https://doi.org/10.3390/electronics13112058 - 25 May 2024
Viewed by 255
Abstract
In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, [...] Read more.
In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive training through a trial-and-error strategy across conceivable reward functions, which is a process notorious for its time-consuming nature and substantial financial implications. Contrary to conventional methodologies, the Monte Carlo Tree Search (MCTS) enables the prediction of reward function quality through Monte Carlo simulations, thereby eliminating the need for exhaustive training on all available reward functions. The findings obtained from MCTS simulations can be effectively leveraged to selectively train only the most suitable RL models. This approach helps alleviate the resource-heavy nature of traditional RL processes through altering the training pipeline. This paper validates the theoretical framework concerning the unique property of the Monte Carlo Tree Search algorithm by emphasizing its generality through highlighting crossalgorithmic and crossenvironmental capabilities while also showcasing its potential to reduce training costs. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
17 pages, 3510 KiB  
Article
A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers
by Jianzhong Yang, Song Liu, Shili Xuan and Huirong Chen
Electronics 2024, 13(10), 1904; https://doi.org/10.3390/electronics13101904 - 13 May 2024
Viewed by 522
Abstract
To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error [...] Read more.
To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
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23 pages, 4558 KiB  
Article
Diachronic Semantic Tracking for Chinese Words and Morphemes over Centuries
by Yang Chi, Fausto Giunchiglia and Hao Xu
Electronics 2024, 13(9), 1728; https://doi.org/10.3390/electronics13091728 - 30 Apr 2024
Viewed by 437
Abstract
Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale [...] Read more.
Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale historical corpus, for instance, analyzing which senses appear, increase, or decrease at which times. However, there is still a lack of Chinese diachronic corpus and dataset in this field to support supervised learning and text mining, and at the method level, few existing works analyze the Chinese semantic changes at the level of morpheme. This paper constructs a diachronic Chinese dataset for semantic tracking applications spanning 3000 years and extends the existing framework to the level of Chinese characters and morphemes, which contains four main steps of contextual sense representation, sense identification, morpheme sense mining, and diachronic semantic change representation. The experiment shows the effectiveness of our method in each step. Finally, in an interesting statistic, we discover the strong positive correlation of frequency and changing trend between monosyllabic word sense and the corresponding morpheme. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
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26 pages, 1194 KiB  
Article
Multicriteria Machine Learning Model Assessment—Residuum Analysis Review
by Jan Kaniuka, Jakub Ostrysz, Maciej Groszyk, Krzysztof Bieniek, Szymon Cyperski and Paweł D. Domański
Electronics 2024, 13(5), 810; https://doi.org/10.3390/electronics13050810 - 20 Feb 2024
Viewed by 676
Abstract
The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the [...] Read more.
The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
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15 pages, 771 KiB  
Article
PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer
by Sultan Asiri, Yang Xiao and Tieshan Li
Electronics 2024, 13(1), 30; https://doi.org/10.3390/electronics13010030 - 20 Dec 2023
Cited by 1 | Viewed by 950
Abstract
Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing [...] Read more.
Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection. Full article
(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)
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